课程信息

第九期2017年北京大学可视化发展前沿研究生暑期学校将于2017年7月8 - 14 日举行。报到日期:7月8日下午。上课时间:7月9-14日上午9:00-12:00,下午14:00-17:00。北京大学可视化发展前沿研究生暑期学校由教育部机器感知与智能重点实验室承办。暑期学校面向全国招生,国内各大院校和研究院所中相关专业的在校硕士、博士研究生和青年教师均可申请,同时可接受少量对可视化领域有浓厚兴趣的优秀高年级本科生。学员参加本项目课程的学习并通过相关的考核将获得由北京大学印制的研究生暑期学校结业证书,并按规定根据学员课程学习完成情况可计4学分研究生成绩。本次暑期学校的重点是大数据的可视化与可视分析。邀请海内外在可视化研究领域具有深厚造诣的知名学者,面向研究生和青年教师系统探讨本领域的前沿理论和研究方法。

招生信息

招生对象

暑期学校招收正式学员60-90人,重点招收可视化相关方向硕士研究生、博士研究生。 同时可接受少量青年教师和非高校学员。

招生方式

学员选拔采取自由报名,择优录取的方式。研究生报名需要有导师的推荐信。报名材料由专家委员会审查后决定录取名单。

授课方式

暑期学校授课由教师授课和学生讨论的形式相结合,强调学员的参与,提供多种互动和参与的机会。授课语言为英文。暑期学校毕业要求完成一定的可视化设计工作。

学分学费

经过考核合格,学员获得暑期学校结业证书。国内在校注册学生参加暑期学校学费1400元。暑期学校根据报名情况提供部分非学生身份学费4500元;学费在发出录取通知后支付,开学领取发票。开具发票后不能退款。

提交申请材料

请在提交申请材料页面填写所需信息后,请推荐人将推荐信电子邮件提交到到邮箱pkuvis[at]pku.edu.cn,即完成全部申请。经过专家委员会审核通过的申请将会收到通知。

课程日程

日期 时间 内容 地点
7 月 08 日 14:00-17:00 Student Registration Room216, Science Building #2
(理科二号楼216)
7 月 09 日 09:00-10:00 Icebreaker and Introduction
Xiaoru Yuan, Peking University
Room 307, Teaching Building #2
(二教307)
10:00-12:00 TBD
Qu Huamin, Hong Kong University
14:00-15:30 TBD
Qu Huamin, Hong Kong University
15:30-17:00 Visual Analytics via Real-time Interactive 2D Embedding
Jaegul Choo, Korea University
7 月 10 日 09:00-12:00 Visual Analytics Via Real-time Interactive 2D Embedding
Jaegul Choo, Korea University
14:00-17:00 Graphs in Scientific Visualization
Chaoli Wang, University of Notre Dame
7 月 11 日 09:00-10:30 Graphs in Scientific Visualization
Chaoli Wang, University of Notre Dame
10:30-12:00 Immersive Analytics
Lu Aidong, University of North Carolina
14:00-17:00 Security Visualization
Lu Aidong, University of North Carolina
7 月 12 日 09:00-12:00 Interactive Visual Anomaly Detection and its Applications
Cao Nan, TongJi University
14:00-17:00 Lab Visit Room 2306, Science Building #2
(理科二号楼2306)
7 月 13 日 09:00-12:00 Interactive Model Analysis
Liu Shixia, Tsinghua University
Room 307, Teaching Building #2
(二教307)
14:00-17:00 TBD
Zhang Jiawan, Tianjing University
7 月 14 日 09:00-12:00 Artistic Data Visualization in the Making
Xu Ruige, Syracuse University
14:00-17:00 Project Report

课程内容

    Xiaoru Yuan
    Title: Icebreaker and Introduction
    Time: July 09, 09:00 – 10:00
    Place: Room 307, Teaching Building #2 (二教307)

    Prof. Qu Huamin
    Title: TBD
    Time: July 09, 10:00 - 12:00 & July 09, 14:00 - 15:30
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: TBD

    Prof. Jaegul Choo
    Title: Visual Analytics via Real-time Interactive 2D Embedding
    Time: July 09, 15:30 – 17:00 & July 10, 09:00 - 12:00
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: The lecture covers basic and advanced techniques of real-time interactive 2D embedding of high-dimensional data. I will describe various 2D embedding methods ranging from multi-dimensional scaling to t-distributed stochastic neighbor embedding, and its visual analytic applications for real-time interactive 2D embedding.

    Prof. Chaoli Wang
    Title: Graphs in Scientific Visualization
    Time: July 10, 14:00 - 17:00 & July 11, 09:00 - 10:30
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: An overview of the use of various graph representations and techniques for scientific visualization with case studies of several graph-based visual interfaces (iTree, TransGraph, FlowGraph) from my research.

    Prof. Aidong Lu
    Title: Immersive Analytics
    Time: July 11, 10:30 - 12:00
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: The course will cover recent work on using virtual/augmented reality devices for data analysis tasks from IEEE VR, ISMAR, and IA venues.

    Title: Security Visualization
    Time: July 11, 14:00-17:00
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: The course will present a set of security visualization techniques from the past VisWeek, VizSec, and data mining venues.

    Prof. Can Nan
    Title: Interactive Visual Anomaly Detection and its Applications
    Time: July 12, 09:00 - 12:00
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: In this lecture, we will introduce visualization techniques that aredesigned and developed for supporting anomaly detection in various application domains such as social media, urban computing, and health informatics. The visualization design principles and guidelines that are specifically proposed for anomaly detection will also be introduced via concrete examples during the lecture.

    Prof. Shixia Liu
    Title: Interactive Model Analysis
    Time: July 13, 09:00-12:00
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: In most AI applications, machine learning models are often treated as a black box. Because of lacking of a comprehensive understanding of the working mechanism of these models, it is hard to build an effective two-communication between a human and a computer, which limits the further adoption of the models. To solve this problem, we have developed a set of visual analytics approaches to help users understand, diagnose, and refine a machine learning model. This talk presents the major challenges of interactive machine learning and exemplifies the solutions with several visual analytics techniques and examples. In particular, we mainly focus on introducing the following three aspects: 1) create a suite of machine learning techniques that produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); 2) develop a set of visual analytics techniques that enable human users to understand and diagnose machine learning models; 3) a semi-supervised model refinement mechanism. Based on these, we develop an interactive model analysis framework, which is exemplified by deep learning, ensemble learning, and the topic model.

    Prof. Jiawan Zhang – TBD
    Title: TBD
    Time: July 13, 14:00-17:00
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: TBD

    Prof. Xu Ruige
    Title: Artistic Data Visualization in the Making
    Time: July 14, 09:00-12:00
    Place: Room 307, Teaching Building #2 (二教307)
    Abstract: In recent years we have seen an increasing of interest in data visualization in the artistic community. Many data-oriented artworks use sophisticated visualization techniques to express a point of view or persuasive goal. Meanwhile the attitude that visualizations can be used to persuade as well as analyze has been embraced by more people in the information visualization community. This talk shares my experience and reflection in creating data visualization as artwork via case study of recent projects. It presents a workflow from conceptual development, data analysis, to algorithm development, procedural modeling, and then final image production. It hopes to offer insight into the artist’s effort of finding balance between persuasive goals and analytic tasks. Furthermore, it raises the question of the roles of artistic data visualization played in assisting people to comprehend data and the influence of this artistic exploration in visualization might have injected in shifting public opinions.

特邀讲师

袁晓如

北京大学

屈华民

香港科技大学

Chaoli Wang

University of Notre Dame

Aidong Lu

University of North Carolina

刘世霞

清华大学

曹楠

同济大学

张加万

天津大学

Jaegul Choo

Korea University

Ruige Xu

Syracuse University

历届讲者

Peter Eades

University of Sydney

Torsten Möller

University of Vienna

Jinwook Seo

Seoul National University

Kai Xu

Middlesex University

Xiaolong Zhang

Pennsylvania State Univ.

Seokhee Hong

University of Sydney

Lei Shi

中科院软件所

Hanqi Guo

Argonne National Lab.

Natalia Andrienko

IAIS

Gennady Andrienko

IAIS

Huamin Qu

HKUST

HanWei Shen

The Ohio State Univ.

Yifan Hu

AT&T Labs

Wei Chen

Zhejiang Unviersity

Claudio T. Silva

CUSP, USA

Ye Zhao

Kent State University

Baoquan Chen

Shandong University

Klaus Mueller

Stony Brook Unviersity

Daniel Keim

Universität Konstanz

Maurizio Patrignani

Roma Tre University

Yingqing Xu

Tsinghua University

Gary Meyer

University of Minnesota

Alfred Inselberg

Tel Aviv University

Hans Hinterberger

ETH Zürich

Jing Yang

Univ. of North Carolina

Michael McGuffin

ÉTS

  历届活动掠影